Learning Deep Latent Spaces for Multi-Label Classification

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the introduction of label-correlation sensitive loss function for recovering the predicted label outputs. Our C2AE is achieved by integrating the DNN architectures of canonical correlation analysis and autoencoder, which allows end-to-end learning and prediction with the ability to exploit label dependency. Moreover, our C2AE can be easily extended to address the learning problem with missing labels. Our experiments on multiple datasets with different scales confirm the effectiveness and robustness of our proposed method, which is shown to perform favorably against state-of-the-art methods for multi-label classification.Comment: published in AAAI-201

ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering

We propose a novel attention based deep learning architecture for visual question answering task (VQA). Given an image and an image related natural language question, VQA generates the natural language answer for the question. Generating the correct answers requires the model's attention to focus on the regions corresponding to the question, because different questions inquire about the attributes of different image regions. We introduce an attention based configurable convolutional neural network (ABC-CNN) to learn such question-guided attention. ABC-CNN determines an attention map for an image-question pair by convolving the image feature map with configurable convolutional kernels derived from the question's semantics. We evaluate the ABC-CNN architecture on three benchmark VQA datasets: Toronto COCO-QA, DAQUAR, and VQA dataset. ABC-CNN model achieves significant improvements over state-of-the-art methods on these datasets. The question-guided attention generated by ABC-CNN is also shown to reflect the regions that are highly relevant to the questions

Privacy Preserving Utility Mining: A Survey

In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page

Improving Formwork Engineering Using the Toyota Way

Construction is a labor-intensive industry with formwork engineering requiring a disproportionate amount of labor and costs. Formwork accounts for approximately one-third of the cost of reinforced concrete construction, partly because traditional formwork processes frequently result in delivery delays and material waste. The purpose of this research is to adapt production concepts pioneered by Toyota (the “Toyota Way”) to improve formwork engineering. The Toyota Way of production consists of four tiers of management philosophy, known as the “4Ps” model. This research adopts the 4Ps as steps for formwork improvement. The first step, “establishing long term vision,” emphasizes long term considerations for formwork improvement. Step two, “establishing value streams,” reviews formwork flows and eliminates wastage. The third step, “developing the crew,” forms mold workers as a team. The final step is “developing a culture of continuous improvement” that provides a basis for constant review and provides a basis for continuous progress. The present research used the Toyota Way to improve formwork engineering. The improvements include reductions in resource waste and increases in operational value. In the long run, the proposed model could provide a learning and growth platform for individuals, the business unit, and the company’s extended network of partners. It could also serve to spur innovative thinking in the improvement of formwork engineering

Concept and Feasibility of One-Embedded System Payload Including Baseband Communication

Traditional approach of payload design develops modules separately such as control, compression and communication. Due to increasing demand of shorter development cycles and lower cost, we shall develop a highly adaptive approach for payload implementation so that we can update it in a short time according to the need of a new mission. Besides, the optimization of payload performance and communication link together becomes possible. Based on these, we propose a “one-embedded system” payload approach. All the control, file management, processing such as compression, and communications are implemented in one built-in embedded system. In other words, after the sensor signal is converted as digital data (after ADC, analog-to-digital-converter), the data gets into the proposed embedded system. And the system “does everything” and then outputs data to DAC (digital-to-analog-converter) and then transmitted it in analog form. The proposed embedded system includes a FPGA implementing a processor IP. Due to the programmable characteristic of FPGA, hardware interfaces can be adjusted quickly according to various mission requirements. Besides, because of the flexibility and adaptability of software, code can be updated to optimize performance according to various tasks during flight. In this work, we provide concept, guideline of optimization, structure, feasibility, benefits and risks of one-embedded system payload approach. An example of implementation for optical remotes sensing payload including interfaces will be investigated

High-Mobility Pentacene-Based Thin-Film Transistors With a Solution-Processed Barium Titanate Insulator

Abstract—Pentacene-based organic thin-film transistors (OTFTs) with solution-processed barium titanate (Ba1.2Ti0.8O3) as a gate insulator are demonstrated. The electrical properties of pentacene-based TFTs show a high field-effect mobility of 8.85 cm2 · V−1 · s−1, a low threshold voltage of −1.89 V, and a low subthreshold slope swing of 310 mV/decade. The chemical composition and binding energy of solution-processed barium titanate thin films are analyzed through X-ray photoelectron spectroscopy. The matching surface energy on the surface of the barium titanate thin film is 43.12 mJ · m−2, which leads to Stranski–Krastanov mode growth, and thus, high mobility is exhibited in pentacene-based TFTs. Index Terms—Barium titanate, high field-effect mobility, high permittivity, organic thin-filmtransistor (OTFT), solution process